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Talk by Jeff Hawkins and Subutai Ahmad of Numenta, Inc.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: The neocortex exhibits a detailed architecture that is largely preserved across different functional areas and between species. This has led to the idea of a common cortical algorithm that underlies all aspects of perception, language, and thought. Whether such a common algorithm exists and what it could be has been debated for decades. Our team has...
Topics: theoretical neuroscience, cortex
Talk given by Stéphane Deny of Facebook AI Research.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley via Zoom.  There are two video files because due to technical problems, there was a break in the recording during the discussion after the main talk. Abstract: Why is visual information transmitted through many parallel channels in the optic nerve, with each channel encoding a different feature-map of the visual scene? Why do neurons in the retina prefer disk-shaped...
Topics: computational neuroscience, vision, retina, machine learning, AI
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Feb 11, 2021
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Lecture 29 (Dec 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is N eural oscillations and synchrony Binding by synchrony Coupled oscillator networks Computing with waves This is the final lecture of the class.
Topics: theoretical neuroscience, neural oscillations, synchrony
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Feb 11, 2021
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Lecture 28 (Dec 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is hyperdimensional computing (lecture by Spencer Kent and Navneedh Maudgalya; continues lecture 27 given Dec 1) Local vs. distributed representation Binding, bundling and sequencing Computing in superposition
Topics: theoretical neuroscience, hyperdimensional computing
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Feb 11, 2021
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Lecture 27 (Dec 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hyperdimensional computing Local vs. distributed representation Binding, bundling and sequencing Computing in superposition
Topics: theoretical neuroscience, hyperdimensional computing
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Feb 11, 2021
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Lecture 26 (Nov 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Reinforcement learning and basal ganglia (lecture by Sophia Sanborn)
Topics: theoretical neuroscience, reinforcement learning, basal ganglia
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Feb 11, 2021
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Lecture 25 (Nov 19) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Supervised learning (and cerebellum) Delta learning rule and back-propagation Supervised learning in cerebellum Kanerva’s Sparse Distributed Memory Model
Topics: theoretical neuroscience, supervised learning, cerebellum, sparse distributed memory, delta...
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Feb 11, 2021
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Lecture 24 (Nov 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Perception-action loop Active perception Representations for perception vs. action Thalamocortical loop as sensorimotor loop
Topics: theoretical neuroscience, perception-action loop
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Feb 11, 2021
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Lecture 23 (Nov 12) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Project mini-presentations
Topic: theoretical neuroscience
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Feb 11, 2021
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Lecture 22 (Nov 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance (Lecture is by Sophia Sanborn; topic continues lecture 21 given Nov 5) Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum
Topics: theoretical neuroscience, hierarchical models
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Feb 11, 2021
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Lecture 21 (Nov 5) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum
Topics: theoretical neuroscience, hierarchical models
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Feb 11, 2021
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Lecture 20 (Nov 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference – denoising, dynamical models Denoising with a sparse coding prior Dynamical models
Topics: theoretical neuroscience, inference, dynamic models
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Feb 11, 2021
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Lecture 19 (Oct 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Probabilistic models Learning and inference in generative models Boltzmann machines Restricted Boltzmann machines and Energy-based models
Topics: theoretical neuroscience, generative models, boltzmann machines
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Feb 11, 2021
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Lecture 18 (Oct 27) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics (continues lecture 17 given Oct 22) Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’
Topics: theoretical neuroscience, Hopfield networks, attractor dynamics
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Feb 11, 2021
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Lecture 17 (Oct 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’
Topics: theoretical neuroscience, hopfield networks, attractor dynamics
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Feb 11, 2021
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Lecture 16 (Oct 20) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are  Organization, topography (continues lecture 15 given Oct 15) Horizontal connections Self-organizing maps Manifold models
Topics: theoretical neuroscience, self-organizing maps
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Feb 11, 2021
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Lecture 15 (Oct 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are  Organization, topography Horizontal connections Self-organizing maps Manifold models
Topics: theoretical neuroscience, self-organizing maps
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Feb 11, 2021
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Lecture 14 (Oct 13) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Inference (continues lectures 12 & 13 given Oct 6 & Oct 8) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Feb 11, 2021
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Lecture 13 (Oct 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Inference (continuation of lecture 12 given Oct 6) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Feb 11, 2021
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Lecture 12 (Oct 6) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Inference LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Feb 11, 2021
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Lecture 11 (Oct 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Neural coding in Lateral Geniculate Nucleus (LGN) (Lecture given by Fritz Sommer)
Topics: theoretical neuroscience, Lateral Geniculate Nucleus, neural coding
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Feb 11, 2021
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Lecture 10 (Sept 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Starburst amacrine cells and the computation of motion (lecture by Rowland Taylor)
Topics: retina, motion detection, amacrine cells, theoretical neuroscience
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Feb 11, 2021
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Lecture 9 (Sept 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Auditory coding Cochlea and auditory nerve Time-frequency analysis Phase and amplitude coding by spikes ICA of natural sound
Topics: theoretical neuroscience, auditory coding
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Feb 10, 2021
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Lecture 8 (Sept 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Retinal tiling Cone tiling Subdivision of labor by midget and parasol RGC’s Foveated sampling
Topics: theoretical neuroscience, retina
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Feb 10, 2021
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Lecture 7 (Sept 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Neural coding (continues lecture 6 given Sept 15) Spiking neuron models Signal compression in retina:  theory of retinal whitening
Topics: theoretical neuroscience, neural coding
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Feb 10, 2021
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Lecture 6 (Sept 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Neural coding: Spiking neuron models Signal compression in retina:  theory of retinal whitening
Topics: theoretical neuroscience, retina, neural coding
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Feb 10, 2021
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Lecture 5 (Sept 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Physics of computation Horizontal cells and lateral inhibition; Optimal pooling by bipolar cells Analog VLSI and silicon retina
Topics: theoretical neuroscience, retina, lateral inhibition
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Feb 10, 2021
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Lecture 4 (Sept 8) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is  Signal detection and amplification Computing with chemistry/allostery Phototransduction
Topics: theoretical neuroscience, phototransduction, signal detection
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Feb 10, 2021
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Lecture 3 (Sept 3) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural mechanisms and models Membrane equation, compartmental model of a neuron Shunting inhibition, NMDA, dendritic nonlinearities Perceptron model
Topics: theoretical neuroscience, neuron models, perceptron model, neuroscience
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Feb 10, 2021
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Lecture 2 (Sept 1) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ This lecture (in two video files) is about Brains Mammalian brain organization Insect and spider brains
Topics: neuroscience, brain organization
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Feb 10, 2021
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Lecture 1 (Aug 27) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020.  Syllabus for the course is at:  https://redwood.berkeley.edu/courses/vs265/ Topics is Introduction Theory and modeling in neuroscience Goals of AI/machine learning vs. theoretical neuroscience 
Topics: theoretical neuroscience, machine learning
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Lecture by Spencer Kent and Navneedh Maudgalya in the "Neural Computation" course (Vision Science 265) Fall semester 2020. 
Topics: neural networks, artificial intelligence, theoretical neuroscience, vector symbolic architectures,...
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Talk by Denis Kleyko of the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Many neural network models have been successful at classification problems, but their operation is still treated as a black box. In the talk, I will describe a theory for one-layer perceptrons that can predict performance on classification tasks. This theory is a  generalization of an existing theory for predicting the performance of some Echo State Networks and connectionist models for symbolic...
Topics: theoretical neuroscience, perceptron, neural networks, machine learning, artificial intelligence
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Dissertation talk by Spencer Kent of the Redwood Center for Theoretical Neuroscience at UC Berkeley, given to the Redwood Center. Abstract: Perception and cognition are teeming with signals and concepts that interact in a multiplicative way, and it is largely this pattern of combination that generates the awesome variability and complexity of our physical and mental worlds. Human brains are prodigious in contending with such complexity, in part due to their ability to factor concepts into more...
Topics: theoretical neuroscience, vector symbolic architectures, high-dimensional computing, neural...
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Talk by Pentti Kanerva given at a meeting of The Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: The standard model of computing treats variables very differently from their values.  Variables are addresses to memory, and values are the contents of the addressed memory locations.  This model of computing has proved to be extremely successful but also poor for modeling the brain's computing.  Neither does it look like what we see happening in brains.  Rather,...
Topics: high dimensional computing, theoretical neuroscience
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Dec 7, 2020
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This item has been deleted and replaced by: https://archive.org/details/Redwood_Center_2020_02_07_Pentti_Kanerva
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Talk by Friedrich Sommer of UC Berkeley.  Given at a meeting of the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract:  Connectionism is a movement in Psychology that began in the 1980ies to understand cognition using neural network models operating on distributed representations.  Connectionism not only greatly advanced traditional neural networks, i.e. working out error back propagation, it also identified their limitations. Importantly, traditional neural networks lack...
Topics: vector symbolic architecture, theoretical neuroscience
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Talk by Jacob Yates of the University of Maryland, Department of Biology.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Most of the core computational concepts in visual neuroscience come from studies using anesthetized or fixating subjects. Although this approach is designed to maximize experimental control – by stabilizing the subject’s gaze – the net result is that the most commonly used visual stimulus is a fixation point and little is known...
Topics: primate vision system, eye movements, neurophysiology
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Talk by Zoran Tiganj of Indiana University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Building artificial agents that can mimic human learning and reasoning has been a longstanding objective in artificial intelligence. I will discuss some of the empirical data and computational models from neuroscience and cognitive science that could help us advance towards this goal. Specifically, I will talk about the importance of structured representations of...
Topics: cognitive maps, theoretical neuroscience, learning
Talk by Jeremy England of the Department of Physics, Georgia Institute of Technology.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley, via Zoom. Abstract: Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random, while...
Topic: self-organization
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Talk by Ruben Coen-Cagli, of the Albert Einstein College of Medicine.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract A central goal of vision science is to understand the principles underlying the perception and cortical encoding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much...
Topics: visual cortex, theoretical neuroscience, divisive normalization
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Talk by Victor Boutin of the Institute of Neuroscience of la Timone (INT), Aix-Marseille University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Building models to efficiently represent images is a central problem in the machine learning community. The brain and especially the visual cortex, has long find economical and robust solutions to solve such a problem. At the local scale, Sparse Coding is one of the most successful framework to model neural...
Topics: sparse coding, predictive coding, visual system
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Talk by Rudiger von der Heydt from Johns Hopkins University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.   Abstract   Image understanding is often conceived as a hierarchical process with many levels where complexity and invariance of object selectivity gradually increase with level in the hierarchy. In contrast, neurophysiological studies have shown that figure-ground organization and border ownership coding, which imply understanding of the object structure of...
Topics: neuroscience, vision, visual cortex, object recognition
In this talk, I will first present a signal representation framework called the Sparse Manifold Transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the...
Topics: sparse coding, theoretical neuroscience
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Dec 12, 2019 Berkeley_ICBS_2019_fall_retreat
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Talk by Stan Klein at the UC Berkeley ICBS 2019 Fall retreat.
Topics: neuroscience, evoked potentials
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Dec 12, 2019 Berkeley_ICBS_2019_fall_retreat
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Talk by Jonathan Tsay at the UC Berkeley ICBS Fall retreat.
Topics: neuroscience, vision
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Talk by Kara Emery, from the University of Nevada, Reno.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract An important goal of vision science is to understand the coding strategies underlying the representation of visual information. I will describe experiments and analyses where we have explored these coding strategies using two different approaches. In the first approach, we factor-analyzed individual differences in observers’ color judgments to reveal...
Topics: theoretical neuroscience, color vision, neural coding
Talk by Leah Krubitzer of UC Davis.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Forelimb morphology and use in mammals is extraordinarily diverse.  Evolution has produced wings, flippers, hooves, paws and hands which are specialized for a variety of behaviors such as flying, swimming and grasping to name a few. While there is a wealth of data in human and non-human primates on the role of motor cortex and posterior parietal cortical areas in reaching...
Topics: motor cortex, theoretical neuroscience
Talk by David Freedman, Professor, Department of Neurobiology, The University of Chicago.  Given at the ICBS Colloquium: Friday, Nov. 8, 2019 at UC Berkeley. Abstract Humans and other advanced animals have a remarkable ability to interpret incoming sensory stimuli and plan task-appropriate behavioral responses. This talk will present parallel experimental and computational approaches aimed at understanding how visual feature encoding in upstream sensory cortical areas is transformed across...
Topics: theoretical neuroscience, neural networks
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Talk by Aaron Milstein from the Stanford University School of Medicine, Dept. of Neurosurgery.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract How do neural circuits in the brain accomplish rapid learning? When foraging for food in a previously unexplored environment, animals store memories of landmarks based on as few as one single view. Also, animals remember landmarks and navigation decisions that eventually lead to food, which requires that the brain...
Topics: learning, hippocampus, dendrites
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Talk by Gary Marcus of NYU.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.  Abstract:   Artificial intelligence has a trust problem. We are relying on A.I. more and more, but it hasn’t yet earned our confidence.   Despite the intense recent hype surrounding AI, no current AI system remotely approaches the flexibility of human intelligence; as I will show, even the ability to read at grade-school level eludes current approaches.   Building on my recent synthesis...
Topics: AI, deep learning, cognitive psychology
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Talk by Pam Reinagel from UC San Diego.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In rapid sensory decision-making, the time taken to choose and the accuracy of the choice are related in three distinct ways. First, it takes more time to assess noisy signals, so decisions about weak sensory stimuli are slower, as well as less accurate. Second, for any given stimulus strength, adopting an overall policy of higher stringency will make decisions slower, but...
Topics: theoretical neuroscience, decision making
Talk by Peter Loxley from the University of New England, Armidale, Australia.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Dynamic Programming with Sparse Codes: Investigating a New Computational Role for Sparse Representations of Natural Image Sequences   Dynamic programming (DP) is a general algorithmic approach used in optimal control and Markov decision processes that balances desire for low present costs with undesirability of high future costs when...
Topics: sparse coding, object tracking, machine learning, AI
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Aug 5, 2019
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Talk by David Zipser of the Redwood Center for Theoretical Neuroscience.  Given at the Friday lab meeting for the Redwood Center. Abstract Thirty years ago we developed learning algorithms for recurrent neural networks. Since then I have trained many recurrent networks to simulate a variety of dynamical systems usually with some relevance to neuroscience. A drawback to this approach is the requirement for lots of data from the system being simulated. This is often not available form real...
Topics: dynamical systems, theoretical neuroscience
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Talk by Wujie Zhang from the Yartsev Lab at UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Social interaction is fundamental to our everyday life and that of diverse animals. When two animals interact, they behave in different ways. Thus, to get a full picture of the neural activity underlying each interaction, we need to record from the brains of both animals at the same time. We do so in a highly social mammal, the Egyptian fruit bat, using...
Topics: neuroscience, social interactions, LFP
Talk by Antoni Chan and Janet Hsiao from the City University of Hong Kong, University of Hong Kong.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract We propose a hierarchical EM algorithm for simplifying a finite mixture model into a reduced mixture model with fewer mixture components. The reduced model is obtained by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples. We develop three applications for our mixture...
Topics: Eye movements, hidden markov model, face recognition
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Jun 12, 2019
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Topics: vision, perception, line drawings
Talk by Paul F.M.J. Verschure from the Barcelona Institute of Science and Technology.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Motor control is usually seen as the result of a gradual replacement of feedback by feedforward control. The perceptual states that inform this process are considered to be defined through qualitatively different processes giving rise to the classical distinction between perceptual and behavioral learning. We have addressed...
Topic: theoretical neuroscience
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Talk by Bill Softky, Visiting scholar, Bioengineering Department, Stanford University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.  Note: the slides for the talk are in the Powerpoint file which can be downloaded using the link on this page. Abstract It is a truth not yet universally acknowledged that a self-regulating system which is stable in one environment can become unstable when the environment changes. This truth is called homeostatic fragility. ...
Topics: screen addiction, theoretical neuroscience
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Talk by Tianshi Wang from EECS, UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Over the last few years, there has been considerable interest in Ising machines, ie, analog hardware for solving difficult (NP hard/complete) computational problems effectively. We present a new way to make Ising machines using networks of coupled self-sustaining nonlinear oscillators.  Our scheme is theoretically rooted in a novel result that connects the phase...
Topics: NP Hard, dynamics, oscillator networks
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Talk by David Rolnick from the University of Pennsylvania.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract It is well-known that the expressivity of a neural network depends on its architecture, with deeper networks expressing more complex functions. For ReLU networks, which are piecewise linear, the number of distinct linear regions is a natural measure of expressivity. It is possible to construct networks for which the number of linear regions grows...
Topics: deep learning, machine learning
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Talk by Gautam Agarwal, from the Champalimaud Institute of the Unknown, Lisbon Portugal.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Learning a complex skill requires traversing a potentially enormous search space. While reinforcement learning (RL) algorithms can approach human levels of performance in complex tasks, they require much more training to do so. This may be because humans constrain search problems with prior knowledge, allowing them to more rapidly...
Topic: learning
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Talk by Risi Kondor of the University of Chicago.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Deep neural networks have proved to be extremely effective in image recognition, machine translation, and a variety of other data centered engineering tasks. However, some other domains, such as learning to model physical systems requires a more careful examination of how neural networks reflect symmetries. In this talk we give an overview of recent developments...
Topics: Neural networks, machine learning
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Mar 22, 2019
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Talk by John Baez from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’ — a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative...
Topics: evolution, natural selection, theoretical biology
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Talk by Patrick Kaifosh, PhD, CSO & Co-Founder, CTRL-labs.  Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract  As the nervous system's evolved output, spinal motor neuron activity is from an evolutionary perspective a natural source of signals for a neural interface. Furthermore, the amplification of these signals by muscle fibers allows them to be measured non-invasively with surface electromyography (sEMG). CTRL-labs has developed a wireless wearable...
Topics: brain machine interface, surface electromyography
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Talk by Andrew Glennerster from the University of Reading.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Note: Slides for the talk are in the PDF and POWERPOINT files included here and available for download.  The POWERPOINT file uses a Reading font so it may not display perfectly if that font is not available, but it does include the animations.  The PDF should always display correctly, but it does not include the animations. Abstract It is clear that animals do...
Topics: animal vision, theoretical neuroscience
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Talk by Steve Piantadosi, UC Berkeley, Dept. of Psychology.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract I’ll present an approach from mathematical logic which shows how sub-symbolic dynamics may give rise to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This approach posits that learners posses a system for e xpressing isomorphisms with which they create mental models with arbitrary dynamics. The...
Topics: theoretical neuroscience, cognition, knowledge representation
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Talk by James Cooke from UCL.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Perception has long been considered to be an inference operation in which internal models of the sensory environment are constructed through experience and are subsequently used in order to assign sensory stimuli to appropriate object categories. When confronted with noisy or incomplete sense data, these internal models are used to identify the most likely object category that might...
Topics: auditory perception, neuroscience
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Talk by Stella Yu from ETH Zurich.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Computer vision has advanced rapidly with deep learning, achieving super-human performance on a few recognition benchmarks.  At the core of the state-of-the-art approaches for image classification, object detection, and semantic/instance segmentation is sliding-window classification, engineered for computational efficiency.  Such piecemeal analysis of visual perception often...
Topics: computer vision, machine learning, deep learning
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Talk by Yulia Sandamirskaya from ETH Zurich.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Activity of neuronal populations in several cortical regions can be described by a Dynamic Neural Field (DNF) equation. A DNF is a continuous in time and in space activation function defined over a metric space spanned over perceptual (e.g., color, retinal location, orientation) or motor (e.g., orientation of the head, direction of movement) dimensions, in which...
Topics: neural networks, Dynamic Neural Field
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Talk by John Co-Reyes from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Sparse reward and long horizon tasks are among the most interesting yet challenging problems to solve in reinforcement learning. I will discuss recent work leveraging representation learning to tackle these sets of problems. We present a novel model which learns a latent representation of low-level skills by embedding trajectories with a variational autoencoder. Skills...
Topic: reinforcement learning
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Talk by Carlos Brody from Princeton University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: I will describe studies of the neural bases of cognitive processes. Rodents, mostly rats, are trained to perform behaviors that lend themselves to quantitative modeling that can help identify and assess specific cognitive processes, such as decision-making, short-term memory, planning, and executive control. With these well-quantified behaviors in hand, we then...
Topics: computational neuroscience, decision-making
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Talk by Shiming Tang from Peking University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract My lab focuses on the neural mechanism of visual object recognition and developing techniques for neuronal circuit mapping. We have established long-term two-photon imaging in awake monkeys — the first and critical step toward comprehensive circuit mapping — to identify single neuron functions. We have systematically characterized the V1 neuronal responses with...
Topics: visual cortex, V1
Talk by Gamaleldin Elsayed from Google Brain.   Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples...
Topic: machine learning
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Talk by Peter Bickel from UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: (Joint work with Gil Kur and Boaz Nadler) The notion of projection pursuit according to Huber (1985) appeared first in work of J.B. Kruskal (1969,1972) and was implemented and popularized by Friedman and Tukey (1974).  Key papers are Huber (1985) and Diaconis and Freedman (1984).  The notion, crudely,  according to Huber, is that a p dimensional sample  can be studied...
Topic: dimensionality reduction
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Talk by Russ Webb from Apple, Inc.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract A primary goal of this work is to give a clear example of the limits of current, deep-learning techniques and suggest how progress can be made.  The presentation will include a discussion of open questions, unpublished experiments, suggestions on how to make progress. This work is founded on the paper Knowledge Matters: Importance of Prior Information for Optimization by...
Topics: machine learning, neural networks
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Talk by Aviv Tamar who is a postdoc at UC Berkeley’s Artificial Intelligence Research lab.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract How can we build autonomous robots that operate in unstructured and dynamic environments such as homes or hospitals? This problem has been investigated under several disciplines, including planning (motion planning, task planning, etc.), and reinforcement learning. While both of these fields have witnessed tremendous...
Topics: Reinforcement Learning, planning, robotics, AI
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Sep 17, 2018
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Talk by Juergen Jost from MPI for Mathematics in the Sciences, Leipzig.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract   In many situations, two or more sources have some information about a target. For instance, sensory input and context information can jointly determine the firing pattern of a neuron. Since the information from the two sources is typically not identical, one wishes to decompose it in those parts that are unique to each source, what is...
Topic: Information theory
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Jul 20, 2018
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Talk by Tori Sharma, PhD, of UC Berkeley.  Given at the 2018 Berkeley course in mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: research ethics, responsible research
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Talk by David Foster of UC Berkeley.  Given to the Berkeley course in mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: hippocampus, place cells
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Lectures given by Maneesh Sahani of UCL in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: PCA, factor analysis, computational neuroscience
Talk by Christoph Schreiner of UCSF.  Given to the Berkeley course on  mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topics: auditory cortex, neuroscience
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Talk by Dan Feldman of UC Berkeley.  Given to the Berkeley course on mining and modeling neuroscience data, held July 9-20 at UC Berkeley.
Topic: sensory cortex
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Lectures given by Stephanie Palmer for the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topic: spike sorting
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Talk by Michael Silver of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July9-20 at UC Berkeley.
Topics: brain activity, human vision, spatial attention
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Lectures given by Odelia Schwartz for the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: image statistics, normative models, vision, theoretical neuroscience
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Videos of the lectures given by David Sussillo of Google in the 2018 Berkeley course in mining and modeling neuroscience data are NOT publicly available.  However, a video of a lecture he gave that includes the same content of his main lecture in the course is at: https://simons.berkeley.edu/talks/david-sussillo-3-22-18 (abstract and video) and: https://www.youtube.com/watch?v=08xa7bE5iTQ&feature=youtu.be (video only). Also, the lab exercise David gave in the course is available in the...
Topics: neural networks, neuroscience, LFADS
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Talk by Marla Feller of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 9-20 at UC Berkeley.
Topics: retina development, computational modeling
Lectures given by Frederic Theunissen of UC Berkeley in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: data analysis, neuroscience
Talk by Talk by Michael Yartsev of UC Berkeley.  Given to the Berkeley course on mining and modeling of neuroscience data, held July 9-20 at UC Berkeley.
Topics: bats, language, learning
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Lectures given by Robert Kass from Carnegie Mellon University in the 2018 Berkeley course in mining and modeling of neuroscience data.  The course was held on July 9-20, 2018 at UC Berkeley in Berkeley California.
Topics: computational neurosciece, course
Talk by Reza Abbasi-Asl of UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In the past decade, research in machine learning has been exceedingly focused on the development of algorithms and models with remarkably high predictive capabilities. Models such as convolutional neural networks (CNNs) have achieved state-of-the-art predictive performance for many tasks in computer vision, autonomous driving, and transfer learning in areas such as...
Topics: Area v4, vision, deep learning
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Talk by from UT Austin and The Simons Institute at UC Berkeley.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Recent results in information theory show how error-correcting codes on large, sparse graphs (“expander graphs”) can leverage multiple weak constraints to produce near-optimal performance. I will demonstrate a mapping between these error-correcting codes and canonical models of neural memory ("Hopfield networks"), and use expander code...
Topics: neural networks, memory, hopfield networks
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Talk by Laurenz Wiskott from the Institut für Neuroinformatik (Ruhr Universität Bochum).  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract    Slow feature analysis (SFA) is a biologically motivated algorithm for extracting slowly varying features from a quickly varying signal and has proven to be a powerful general-purpose preprocessing method for spatio-temporal data in brain modeling as well as technical applications.  We have applied SFA to the learning...
Topics: learning, theoretical neuroscience
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Apr 6, 2018
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Talk by Sophie Deneve of the University of Paris/Simons Institute.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract The brain computes with spikes, and spikes are costly to metabolism. Yet, the spiking responses of most cortical cells appear extremely noisy, to the extent that the only feature that repeats from trial to trial is the firing rate (and only for the small minority of cells that are modulated at all). If that was the case, the most sophisticated of...
Topic: Theoretical Neuroscience
Talk by Yasaman Bahri of Google Brain.  Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Obtaining a better understanding of neural networks with random parameters is relevant for deep learning practice — for instance, by informing good initializations — and is a natural first step in building a more complete base of knowledge within deep learning. I will survey some of our recent work at Google Brain which originated from the study of random neural...
Topics: deep neural networks, neural networks, deep learning
Talk by Arturo Deza of UC Santa Barbara.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Are there any benefits in incorporating the foveated nature of human vision into image-based metrics of perception and computer vision systems? In this talk I hope to advance our understanding of this question through my work via psychophysical experiments (eye-tracking), computational modelling, and computer vision. The first part of the talk will revolve around...
Topics: human vision, computer vision, fovea
Talk by Leenoy Meshulam from Princeton University.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Recent technological progress has dramatically increased our access to the neural activity underlying memory-related tasks. These complex high-dimensional data call for theories that allow us to identify signatures of collective activity in the networks that are crucial for the emergence of cognitive functions. As an example, we study the neural activity in...
Topics: neuroscience, data analysis
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Talk by Joe Makin, UCSF.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract. A common task facing computational scientists and, arguably, the brains of primates more generally is to construct models for data, particularly ones that invoke latent variables. Although it is often natural to identify the latent variables of such a model with the true unobserved variables in the world, the correspondence between the two can be more complicated, as when the former are...
Topics: theoretical neuroscience, statistics, density estimation
Talk by Miguel Lázaro-Gredilla of Vicarious, Inc.  Given to the Redwood Center for Theoretical Neuroscience at UC Berkelely. Abstract: Compositionality, generalization, and learning from a few examples are among the hallmarks of human intelligence. In this talk I will describe how Vicarious combines these ideas to create approaches to CAPTCHA breaking and Atari game playing that improve on the state of the art. Both of these tasks have indeed been tackled before, using respectively...
Topics: AI, machine learning, Recursive Cortical Network, Schema Networks